Research Article
Open Access
Design and Implementation of a Software Engineering-Driven Deep Transfer Learning Framework for Seafood Fish Detection
1
Department of Computer Science, School of Electrical Engineering & Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
2
Department of Computer Science, Riphah School of Computing & Innovation, Riphah International University, Lahore, Pakistan
3
Department of Computer Science, Government College University, Faisalabad, Sahiwal Campus, Sahiwal 57000, Pakistan
4
Department of Computer Science, Illinois Institute of Technology, Chicago, IL, 60616, United States
* Corresponding Author:
Amna Farooq,
[email protected]
Volume 1, Issue 2
2025
Received: 25 July 2025
Accepted: 27 September 2025
Published: 03 November 2025
Article Information
Published in
ICCK Journal of Software Engineering
Volume/Issue
Volume 1, Issue 2, 2025
Pages
109-123
Abstract
Seafood quality inspection is critical for ensuring food safety and minimizing economic losses from spoilage. While traditional methods are slow and labor-intensive, computer vision and machine learning have emerged as efficient automated alternatives. This study presents SFFDNet, a software engineering-driven convolutional neural network featuring a lightweight 19-layer architecture with optimized feature extraction blocks and regularization strategies. With only 2.49 million parameters—significantly fewer than VGG16 (138M) and ResNet50 (25.6M)—our model achieves 98.80% accuracy on the Large-Scale Fish Segmentation and Classification Dataset. SFFDNet outperforms both transfer learning models (VGG16: 96.54%, ResNet50: 53.34%, InceptionV3: 58.39%) and conventional approaches (CNN: 96.00%, SegNet: 88.69%, YOLO+ResNet50: 91.64%). The framework emphasizes computational efficiency, modularity, and scalability, bridging high-accuracy deep learning with practical industrial seafood inspection through software engineering principles.
Graphical Abstract
Keywords
feature extraction
image segmentation
task-specific analysis
quality evaluation
color-based image analysis
classification
food quality inspection
convolutional neural networks
SFFDNet
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
- Vianna, G. M., Zeller, D., & Pauly, D. (2020). Fisheries and policy implications for human nutrition. Current Environmental Health Reports, 7(3), 161-169.
[CrossRef] [Google Scholar] - Kruijssen, F., Tedesco, I., Ward, A., Pincus, L., Love, D., & Thorne-Lyman, A. L. (2020). Loss and waste in fish value chains: A review of the evidence from low and middle-income countries. Global Food Security, 26, 100434.
[CrossRef] [Google Scholar] - Sagar Naik, C., Mohan, C. O., Remya, S., Kishore, P., & Bindu, J. (2025). Artificial Intelligence Tools for Processing and Quality Detection of Fish and Fisheries Products.
[CrossRef] [Google Scholar] - Kibriya, H., Rafique, R., Ahmad, W., & Adnan, S. M. (2021, January). Tomato leaf disease detection using convolution neural network. In 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) (pp. 346-351). IEEE.
[CrossRef] [Google Scholar] - Wang, J., Yue, H., & Zhou, Z. (2017). An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food control, 79, 363-370.
[CrossRef] [Google Scholar] - Tao, X., Li, H., Lu, J., & Wang, X. (2012, August). Computer vision technology for seafood quality evaluation. In 2012 International Conference on Computer Science and Service System (pp. 1888-1891). IEEE.
[CrossRef] [Google Scholar] - Linsen, Z., Guanglei, S., & Junbiao, D. (2006). Research on management of traceability of beef safety system through the Internet based on JSP. Chinese Agricultural Science Bulletin, 22(12), 13-16.
[Google Scholar] - Wang, Y., Zhang, H., Shi, J., Wang, S., Zhou, M., & Wang, D. (2015). Quality information traceability system based on seafood's production process. Transactions of the Chinese Society of Agricultural Engineering, 31(14), 264-271.
[CrossRef] [Google Scholar] - Yan, B., Shi, P., & Huang, G. (2013). Development of traceability system of aquatic foods supply chain based on RFID and EPC internet of things. Transactions of the Chinese Society of Agricultural Engineering, 29(15), 172-183.
[CrossRef] [Google Scholar] - Wang, Y., Yan, H., & Yin, Z. (2016). A quality traceability system for seafood based on bill of lots. Revista de la Facultad de Ingeniería, 31(4), 264-271.
[CrossRef] [Google Scholar] - Nicolae, C. G., Moga, L. M., Bahaciu, G. V., & Marin, M. P. (2017). Traceability system structure design for fish and fish products based on supply chain actors needs. Scientific Papers. Series D. Animal Science, 60.
[Google Scholar] - Cui, S., Zhou, Y., Wang, Y., & Zhai, L. (2020). Fish detection using deep learning. Applied Computational Intelligence and Soft Computing, 2020(1), 3738108.
[CrossRef] [Google Scholar] - Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861.
[Google Scholar] - Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305-6324.
[CrossRef] [Google Scholar] - Rasheed, J. (2021, September). A sustainable deep learning based computationally intelligent seafood monitoring system for fish species screening. In 2021 International conference on artificial intelligence of things (ICAIoT) (pp. 1-6). IEEE.
[CrossRef] [Google Scholar] - Yildiz, M. B., Yasin, E. T., & Koklu, M. (2024). Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application. European Food Research and Technology, 250(7), 1919-1932.
[CrossRef] [Google Scholar] - Kumaravel, B., Amutha, A. L., Mary, T. P., Agrawal, A., Singh, A., Saran, S., & Govindarajan, N. (2025). Automated seafood freshness detection and preservation analysis using machine learning and paper-based pH sensors. Scientific Reports, 15(1), 1-14.
[CrossRef] [Google Scholar] - Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
[Google Scholar] - Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.
[Google Scholar] - Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
[CrossRef] [Google Scholar] - Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).
[CrossRef] [Google Scholar] - Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).
[CrossRef] [Google Scholar] - Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
[CrossRef] [Google Scholar] - He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[CrossRef] [Google Scholar] - Ulucan, O., Karakaya, D., & Turkan, M. (2020, October). A large-scale dataset for fish segmentation and classification. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE.
[CrossRef] [Google Scholar] - Chuang, M. C., Hwang, J. N., & Williams, K. (2016). A feature learning and object recognition framework for underwater fish images. IEEE Transactions on Image Processing, 25(4), 1862-1872.
[CrossRef] [Google Scholar] - dos Santos, A. A., & Gonçalves, W. N. (2019). Improving Pantanal fish species recognition through taxonomic ranks in convolutional neural networks. Ecological Informatics, 53, 100977.
[CrossRef] [Google Scholar] - Jalal, A., Salman, A., Mian, A., Shortis, M., & Shafait, F. (2020). Fish detection and species classification in underwater environments using deep learning with temporal information. Ecological Informatics, 57, 101088.
[CrossRef] [Google Scholar] - Saitoh, T., Shibata, T., & Miyazono, T. (2016). Feature Points based Fish Image Recognition. International Journal of Computer Information Systems & Industrial Management Applications, 8.
[Google Scholar] - Alsmadi, M. K., Tayfour, M., Alkhasawneh, R. A., Badawi, U., Almarashdeh, I., & Haddad, F. (2019). Robust feature extraction methods for general fish classification. International Journal of Electrical & Computer Engineering (2088-8708), 9(6), 5192-5204.
[CrossRef] [Google Scholar] - Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2818-2826).
[CrossRef] [Google Scholar] - He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778).
[CrossRef] [Google Scholar]
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APA Style
Hameed, M., Haroon, M., Farooq, A., & Ali, A. K. S. (2025). Design and Implementation of a Software Engineering-Driven Deep Transfer Learning Framework for Seafood Fish Detection. ICCK Journal of Software Engineering, 1(2), 109–123. https://doi.org/10.62762/JSE.2025.535801
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TY - JOUR AU - Hameed, Muzzamal AU - Haroon, Muhammad AU - Farooq, Amna AU - Ali, Abdul Karim Sajid PY - 2025 DA - 2025/11/03 TI - Design and Implementation of a Software Engineering-Driven Deep Transfer Learning Framework for Seafood Fish Detection JO - ICCK Journal of Software Engineering T2 - ICCK Journal of Software Engineering JF - ICCK Journal of Software Engineering VL - 1 IS - 2 SP - 109 EP - 123 DO - 10.62762/JSE.2025.535801 UR - https://www.icck.org/article/abs/JSE.2025.535801 KW - feature extraction KW - image segmentation KW - task-specific analysis KW - quality evaluation KW - color-based image analysis KW - classification KW - food quality inspection KW - convolutional neural networks KW - SFFDNet AB - Seafood quality inspection is critical for ensuring food safety and minimizing economic losses from spoilage. While traditional methods are slow and labor-intensive, computer vision and machine learning have emerged as efficient automated alternatives. This study presents SFFDNet, a software engineering-driven convolutional neural network featuring a lightweight 19-layer architecture with optimized feature extraction blocks and regularization strategies. With only 2.49 million parameters—significantly fewer than VGG16 (138M) and ResNet50 (25.6M)—our model achieves 98.80% accuracy on the Large-Scale Fish Segmentation and Classification Dataset. SFFDNet outperforms both transfer learning models (VGG16: 96.54%, ResNet50: 53.34%, InceptionV3: 58.39%) and conventional approaches (CNN: 96.00%, SegNet: 88.69%, YOLO+ResNet50: 91.64%). The framework emphasizes computational efficiency, modularity, and scalability, bridging high-accuracy deep learning with practical industrial seafood inspection through software engineering principles. SN - 3069-1834 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Hameed2025Design,
author = {Muzzamal Hameed and Muhammad Haroon and Amna Farooq and Abdul Karim Sajid Ali},
title = {Design and Implementation of a Software Engineering-Driven Deep Transfer Learning Framework for Seafood Fish Detection},
journal = {ICCK Journal of Software Engineering},
year = {2025},
volume = {1},
number = {2},
pages = {109-123},
doi = {10.62762/JSE.2025.535801},
url = {https://www.icck.org/article/abs/JSE.2025.535801},
abstract = {Seafood quality inspection is critical for ensuring food safety and minimizing economic losses from spoilage. While traditional methods are slow and labor-intensive, computer vision and machine learning have emerged as efficient automated alternatives. This study presents SFFDNet, a software engineering-driven convolutional neural network featuring a lightweight 19-layer architecture with optimized feature extraction blocks and regularization strategies. With only 2.49 million parameters—significantly fewer than VGG16 (138M) and ResNet50 (25.6M)—our model achieves 98.80\% accuracy on the Large-Scale Fish Segmentation and Classification Dataset. SFFDNet outperforms both transfer learning models (VGG16: 96.54\%, ResNet50: 53.34\%, InceptionV3: 58.39\%) and conventional approaches (CNN: 96.00\%, SegNet: 88.69\%, YOLO+ResNet50: 91.64\%). The framework emphasizes computational efficiency, modularity, and scalability, bridging high-accuracy deep learning with practical industrial seafood inspection through software engineering principles.},
keywords = {feature extraction, image segmentation, task-specific analysis, quality evaluation, color-based image analysis, classification, food quality inspection, convolutional neural networks, SFFDNet},
issn = {3069-1834},
publisher = {Institute of Central Computation and Knowledge}
}
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